Bonmati, E;
Hu, Y;
Sindhwani, N;
Dietz, HP;
D'hooge, J;
Barratt, D;
Deprest, J;
(2018)
Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network.
Journal of Medical Imaging
, 5
(2)
, Article 021206. 10.1117/1.JMI.5.2.021206.
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Abstract
Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction, and rest, all labeled by three operators, is used for training and evaluation in a leave-one-patient-out cross validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams’ index of 1.03), and outperforming a U-Net architecture without the need for batch normalization. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semiautomatic approach.
| Type: | Article |
|---|---|
| Title: | Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/1.JMI.5.2.021206 |
| Publisher version: | https://doi.org/10.1117/1.JMI.5.2.021206 |
| Language: | English |
| Additional information: | © 2018 The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License (https://creativecommons.org/licenses/by/3.0/) |
| Keywords: | levator hiatus, automatic segmentation, self-normalizing neural network, ultrasound, convolutional neural network |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10041831 |
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